Haze removal for new generation optical sensors

Abstract

ABSTRACTHaze and cloud contamination is a common problem in optical remote-sensing imagery, as it can lead to the inaccurate estimation of physical properties of the surface derived from remote sensing and reduced accuracy of land cover classification and change detection. Haze optimized transform (HOT) is a methodology applicable to radiometric compensation of additive haze effects in visible bands that exhibits a spatially complex distribution over an image. The generic approach of HOT allows for the use of older satellite imaging sensors that include at least two visible bands (e.g. Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) sensors). This study proposes modifications to extend HOT applicability to new sensors. The improvements and extended functionality adapt the method to the higher radiometric resolution specifications of newer generation sensors and use percentile-based minimum in the correction procedure to avoid causing fake minimum. Alternative filters are also evaluated to smooth raw HOT output and the cloud mask is generated as an additional output. A Landsat 8 scene of Los Angeles is used to demonstrate the improved methodology. The methodology is applicable to sensors such as QuickBird, Worldview 2/3. More than 20 additional scenes were used to evaluate the effectiveness of the methodology.